基於圖形引導幾何注意力的一致性飛行時間深度去噪

摘要

由飞行时间（ToF）传感器捕获的深度图像易受噪声影响，需进行去噪处理以确保下游应用的可靠性。先前的研究要么专注于单帧处理，要么在未考虑帧间对应像素深度变化的情况下进行多帧处理，导致不理想的时间不一致性和空间模糊性。本文提出了一种新颖的ToF深度去噪网络，利用运动不变图融合技术，同时提升时间稳定性和空间清晰度。具体而言，尽管帧间存在深度偏移，图结构展现出时间自相似性，使得跨帧几何注意力机制得以应用于图融合。随后，通过在融合图上引入图像平滑先验，并结合源自ToF噪声分布的数据保真项，我们构建了一个最大后验问题用于ToF去噪。最终，该问题的解被展开为迭代滤波器，其权重通过图引导的几何注意力机制自适应学习，从而形成一个高性能且可解释的网络。实验结果表明，所提方案在合成DVToF数据集上实现了最先进的准确性和一致性表现，并在真实Kinectv2数据集上展现出良好的泛化能力。源代码将发布于https://github.com/davidweidawang/GIGA-ToF。

English

Depth images captured by Time-of-Flight (ToF) sensors are prone to noise, requiring denoising for reliable downstream applications. Previous works either focus on single-frame processing, or perform multi-frame processing without considering depth variations at corresponding pixels across frames, leading to undesirable temporal inconsistency and spatial ambiguity. In this paper, we propose a novel ToF depth denoising network leveraging motion-invariant graph fusion to simultaneously enhance temporal stability and spatial sharpness. Specifically, despite depth shifts across frames, graph structures exhibit temporal self-similarity, enabling cross-frame geometric attention for graph fusion. Then, by incorporating an image smoothness prior on the fused graph and data fidelity term derived from ToF noise distribution, we formulate a maximum a posterior problem for ToF denoising. Finally, the solution is unrolled into iterative filters whose weights are adaptively learned from the graph-informed geometric attention, producing a high-performance yet interpretable network. Experimental results demonstrate that the proposed scheme achieves state-of-the-art performance in terms of accuracy and consistency on synthetic DVToF dataset and exhibits robust generalization on the real Kinectv2 dataset. Source code will be released at https://github.com/davidweidawang/GIGA-ToF{https://github.com/davidweidawang/GIGA-ToF}.

基於圖形引導幾何注意力的一致性飛行時間深度去噪

Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric Attention

摘要

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